Detection system for inclusion defects in hot-rolled steel plates using MFLT with two different magnetizing strengths

Yotsuji, Junichi; Koshihara, Takahiro

doi:10.1063/1.4864973

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…Each IMF signal component must satisfy two eigenvalue conditions, which are illustrated in [28]. Then, the details of the formula of the EMD process, which is also called the screening process of the IMF, can be found in [43].…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

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The detection and classification method of sub-macroscopic defects inside steel with an ultrasonic testing and CatBoost-based stacking model

Zhang

Wang

Chen

2023

Meas. Sci. Technol.

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With the increasing demand for advanced steel is increasing year by year, and the internal cleanness content of steel inclusions becomesis an important evaluation indicator for the evaluation of material material quality. Sub-macroscopicInclusions defects are randomly distributed inside the steel materials, which has a great impact on the performance and quality safety of the steel. In especial, sub-macroscopic inclusions with sizes ranging from 50μm to 400μm have seriously affected material stability and fatigue life because they are not covered by existing testing standards. In addition,Besides, the existing current detection methods for inclusions in steel generally have problems such as low efficiency and complexity process. In this paper, we propose a non-destructive inclusion testing and classification framework basing on ultrasonic testing experiments, signal feature extraction and machine learning methods. Under the optimal experimental detection conditions we found through experiments, a large-scale sub-macroscopic inclusion signal data set is established to realize the classification of defects. Moreover, Empirical Mode Decomposition (EMD) and other feature extraction algorithms are applied to further boost the model performance. We propose a Catboost-based stacking fused model named Stacked-CBT, which obtains state-of-the-art experimental result with accuracy rate of 86.65% and demonstrates that the proposed framework is feasible to classify the sub-macroscopic inclusion signals. To the best of our knowledge, there is no previous study in this field has acquire such large amount of experimental sub-macroscopic signal data while taking into consideration classification-specific designs.

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Section: Empirical Mode Decompositionmentioning

confidence: 99%

“…The four types of sub-macroscopic defects above includes pores and slag inclusions, which may include oxides, sulfides, silicates and their combination [43,[49][50][51][52][53]. The time domain waveforms of four types of defects are distinct, which illustrates the various components of them.…”

Section: Data Set Generation Under Optimal Testing Conditionsmentioning

confidence: 99%

The detection and classification method of sub-macroscopic defects inside steel with an ultrasonic testing and CatBoost-based stacking model

Zhang

Wang

Chen

2023

Meas. Sci. Technol.

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Experimental verification of nondestructive identification of location and thickness of pipe wall thinning by using magnetic sensor

Ashizawa

Kishimoto

Kobayashi

et al. 2017

Transactions of the JSME (In Japanese)

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Replacement of a damaged pipe is important to prevent an accident caused by the pipe. The major damage of the pipe in long-term use is pipe wall thinning. Thus, identification techniques of the location and the thickness of the pipe wall thinning are necessary to decide the appropriate time to replace the pipe. The ultrasonic testing (UT) and the radiographic testing (RT) are currently mainstreams in nondestructive inspections. However, the UT requires the surface preparation of the pipe, and the RT has the risk of the radiation leakage. This study has developed a novel technique of nondestructive identification of location and thickness of pipe wall thinning by using magnetic sensor. This technique aims at only nonmagnetic pipes, and estimates the location and the thickness of the thinning part in the pipe by using the measurement data of the magnetic flux density when the electric current is applied into the pipe. Furthermore, the measurement sensitivity of the magnetic flux density is improved by setting the high magnetic permeability material around the magnetic sensor. In order to verify the validity of the proposed technique, numerical simulations and actual experiments were performed by using specimens with several patterns of the location and the thickness of the thinning part. The results show that the direct analysis values agreed with the measurement data. The inverse analysis results show that the estimated values of the parameters to evaluate the thinning part successfully agreed with the correct values in most of the cases in this study. It is believed that the proposed technique can be applied to practical situations.

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Waste Reduction via Computer Vision-based Inspection: Towards Lean Systems in Metal Production

Shahin¹,

Chen

Hosseinzadeh³

et al. 2023

Preprint

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The global steel demand continues to increase, with steel being used in various industries, including construction, automobile, national defense, and machinery. However, steel production is a delicate process that can result in different defects on the steel surface, negatively affecting the quality of the steel products. Therefore, recognizing metal surface defects is critical in the metal production industry. Manual detection of these defects is the standard method, but it is time-consuming, labor-intensive, and prone to subjective factors, leading to low accuracy and unreliable results. Automated defect detection using computer vision methods can replace or supplement manual detection. In recent years, machine learning algorithms, particularly Convolutional Neural Networks (CNNs), have shown great promise in achieving high accuracy rates in this task. In addition, image classification algorithms can contribute to Lean metal production by identifying defects or anomalies in the manufacturing process, which can be used to reduce waste and increase efficiency. However, the performance and cost of different CNN architectures can vary widely, making it challenging for decision-makers to select the most suitable model. This paper analyzes various CNN-based image classification algorithms, including MobileNet, ShuffleNet, DenseNet, RegNet, and NasNet, in classifying steel surface defects in the NEU-CLS-64 dataset. We evaluate their performance using metrics such as accuracy, precision, sensitivity, specificity, F1 score, and G-mean, and benchmark these models against each other. Our findings revealed that RegNet achieved the highest accuracy, precision, sensitivity, specificity, F1 score, and G-mean performance but at a higher cost than other models. Meanwhile, MobileNet had the lowest performance. The results provide decision-makers with valuable insights into selecting the most suitable CNN model for steel surface defect detection based on their performance.

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Detection system for inclusion defects in hot-rolled steel plates using MFLT with two different magnetizing strengths

Cited by 3 publications

References 1 publication

The detection and classification method of sub-macroscopic defects inside steel with an ultrasonic testing and CatBoost-based stacking model

The detection and classification method of sub-macroscopic defects inside steel with an ultrasonic testing and CatBoost-based stacking model

Experimental verification of nondestructive identification of location and thickness of pipe wall thinning by using magnetic sensor

Waste Reduction via Computer Vision-based Inspection: Towards Lean Systems in Metal Production

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